Tutorial on analysis pipelines for visual experience datasets

Author

Johannes Zauner, Aaron Nicholls, Lisa Ostrin, and Manuel Spitschan

1 Abstract

This tutorial presents an analysis pipeline for visual experience datasets, with a focus on reproducible workflows tailored for use in human chronobiology and myopia research. Light exposure and its retinal encoding affect human physiology and behaviour over various time scales. Here, we provide step-by-step instructions for importing, visualising, and processing light exposure data using the open-source tool LightLogR. This includes time-series analysis for working distance, spectral characteristics, and biologically relevant light metrics. By leveraging a modular approach, this tutorial supports researchers in building flexible and robust pipelines that can accommodate diverse experimental paradigms and measurement systems.

2 Introduction

Exposure to the optical environment — often referred to as visual experience — profoundly influences human physiology and behaviour across multiple time scales. Two notable examples, though from distinct research domains, can be understood through a common, retinally-referenced conceptual framework.

The first relates to the non-visual effects of light on human circadian and neuroendocrine physiology. The light–dark cycle entrains the circadian clock, and light exposure during the night suppresses melatonin production (Brown et al. 2022; Blume, Garbazza, and Spitschan 2019).

The second concerns the influence of visual experience on ocular development, particularly myopia. Time spent outdoors — characterised by distinct optical environments — has been consistently associated with protective effects on ocular growth and health outcomes (Dahlmann-Noor et al. 2025).

In controlled laboratory settings, light exposure can be held constant or manipulated parametrically. However, such exposures rarely replicate real-world conditions, which are inherently complex and dynamic. As people move in and between spaces (indoors and outdoors) and move their trunks, heads and eyes, the exposure to the optical environment varies significantly (Webler et al. 2019), and is modulated by behaviour (Biller, Balakrishnan, and Spitschan 2024). Wearable devices for measuring light exposure have thus emerged as vital tools in capturing the richness of ecological visual experience. These tools generate high-dimensional datasets that demand rigorous and flexible analysis strategies.

Starting in the 1980s (Okudaira, Kripke, and Webster 1983), technology to measure exposure to the optical environment has been developed and matured, with miniaturized illuminance sensors now (2025) being very common in consumer smartwatches. In research, several device and device types are available, which differ in their functionality, ranging from small pin-like devices measuring light exposure (Mohamed et al. 2021) to head-mounted multi-modal measurement devices capturing almost all relevant aspects of visual experience (Gibaldi et al. 2024). With the increased technical capabilities in wearables come considerably complex and dense datasets. These go hand in hand with an overwhelming amount of metrics, as revealed by review papers in both fields.

At present, the analysis processes to derive metrics are often implemented on a by-workgroup, or even by-researcher basis, which is both a potential source of errors and inconsistencies between publications, and also a considerable time sink for researchers (Hartmeyer, Webler, and Andersen 2022). Too often, more time is spent preparing the data than to actually gain insights through rigoruous statistical testing and exploration. The preparation tasks are best handled or at least facilitated by standardized, transparent, and community-based pipelines for analysis (L. A. S. Zauner Johannes AND Udovicic 2024).

In circadian research, the package LightLogR for R statistical software was developed (J. Zauner, Hartmeyer, and Spitschan 2025). LightLogR is an open-source, MIT licensed, and community-driven package specifically made to work with data from wearable light loggers, and optical radiation dosimeters. It also contains functions to calculate over sixty different metrics used in the field of research (Hartmeyer and Andersen 2023). In a recent update the package was significantly expanded to deal with modalities beyond illuminance, like distance or even light spectra, which are highly relevant for myopia research (Hönekopp and Weigelt 2023).

In this article we show that the analysis pipelines and metric functions in LightLogR naturally apply to the whole field of visual experience, not just circadian research and chronobiology. Our approach is modular and extensible, allowing researchers to adapt it to a variety of devices and research questions. Emphasis is placed on clarity, transparency, and reproducibility, aligning with best practices in scientific computing and open science. We use data from two devices to demonstrate the LightLogR workflow and output with metrics relevant in the field of myopia, covering metrics for working distance, daylight exposure, and spectral analyses. It is recommended to recreate the analysis in this script. All necessary data and code are provided under an open license in the GitHub repository.

3 Methods and materials

3.1 Software

This tutorial is built with Quarto, an open-source scientific and technical publishing system, integrating text, code, and code-output into a single document. The source-code to reproduce the outcomes is part of the document and accessible via the code-tools menu.

Package LightLogR (Version 0.9.0 “Sunrise”) was used with R statistical software (Version 4.4.3 “Trophy Case”). We further used the tidyverse package (Version 2.0.0) for principled data analysis, which LightLogR follows. Finally, the gt package (Version 1.0.0) was used for table generation. A comprehensive overview of the R computing environment can be found in the session info

3.2 Metric selection and definitions

In March of 2025, two workshops with researchers in the field of myopia, initiated by the Research Data Alliance (RDA) Working Group on Optical Radiation Exposure and Visual Experience Data focused on the current needs and future opportunities regarding data analysis, including metrics. Out of the expert inputs in these workshops, a list of visual experience metrics was collected, which is shown in Table 1. These include currently used metrics and definitions (Wen et al. 2020, 2019; Bhandari and Ostrin 2020; Williams et al. 2019), but also new metrics that are possible through spectrally-resolved measurements.

Table 1: Overview of metrics to calculate. In all cases, the averages for weekday, weekend, and the mean daily value are calculated through [mean_daily](https://tscnlab.github.io/LightLogR/reference/mean_daily.html).

No.	Name	Implementation¹
	Distance
1	Total wear time daily	durations()
2	Duration of Near work, Intermediate Work, Near + Intermediate Work, or per each Distance range (10cm steps)	filter for distance range + durations() (for single ranges) or grouping by distance range + durations() (for all ranges)
3	Frequency of Continuous near work	extract_clusters() + summarize_numeric()
4	Frequency, duration, and distances of Near Work episodes	extract_clusters() + extract_metric() + summarize_numeric()
5	Frequency and duration of Visual breaks	extract_clusters() + filter
	Light
6	Light exposure (in lux)	summarize_numeric()
7	Duration per Outdoor range	grouping by Outdoor range + durations()
8	The number of times light level changes from indoor (<1000 lx) to outdoor (>1000 lx)	extract_states() + summarize_numeric()
9	Longest period above 1000 lx	period_above_threshold()
	Spectrum
10	Ratio of short vs. long wavelength light	spectral_integration() + summarize_numeric()
11	Short-wavelength light at certain times of day	spectral_integration() + + filter_Time() (for defined times) or cut_Datetime() (for regular time intervals) or add_photoperiod() (for solar times) + grouping by time state + summarize_numeric()

Table 2 contains definitions for the terms in Table 1. Note that these definitions may vary depending on the research question or device capabilities.

Table 2: Definitions of mean daily and conditions for distance and illuminance calculation

Metric	Description / pseudo formula
Total wear time	\(\sum(t)*dt, \textrm{ where } t\textrm{: valid observations }\)
Mean daily	\(\frac{5\bar{\textrm{weekday}} + 2\bar{weekend}}{7}\)
Near work	\(\textrm{working distance}, [10,60)cm\)
Intermediate Work	\(\textrm{working distance}, [60,100)cm\)
Total work	\(\textrm{working distance}, [10,120)cm\)
Distance range	\(\textrm{working distance}, {[10,20)cm \textrm{, Extremely near} \\ [20,30)cm \textrm{, Very near} \\ [30,40)cm \textrm{, Fairly near} \\ [40,50)cm \textrm{, Near} \\ [50,60)cm \textrm{, Moderately near} \\ [60,70)cm \textrm{, Near intermediate} \\ [70,80)cm \textrm{, Intermediate} \\ [80,90)cm \textrm{, Moderately intermediate} \\ [90,100)cm \textrm{, Far intermediate}}\)
Continuous near work	\(\textrm{working distance}, [20,60)cm,\) \(T_\textrm{duration} ≥ 30 minutes, \textrm{ }T_{interruptions} ≤ 1 minute\)
Near work episodes	\(\textrm{working distance}, [20,60)cm,\) \(T_\textrm{interruptions} ≤ 20 seconds\)
Ratio of daily near work	\(\frac{T_\textrm{near work}}{T_\textrm{total wear}}\)
Visual break	\(\textrm{working distance} ≥ 100cm, \\ T_\textrm{duration} ≥ 20 seconds, \textrm{ }T_\textrm{previous episode} ≤ 20 minutes\)
Outdoor range	\(\textrm{illuminance}, {[1000,2000)lx \textrm{, Outdoor bright} \\ [2000,3000)lx \textrm{, Outdoor very bright} \\ [3000, \infty) lx \textrm{, Outdoor extremely bright}}\)
Light exposure²	\(\bar{illuminance}\)
Spectral bands	\(\textrm{spectral irradiance}, {[380,500]nm \textrm{, short wavelength light} \\ [600, 780]nm \textrm{, long wavelength light}}\)
Ratio of short vs. long wavelength light	\(\frac{E_{e\textrm{,short wavelength}}}{E_{e\textrm{,long wavelength}}}\)

3.3 Devices

Data from two devices will be used for analysis:

Distance and light metrics will be calculated based on export from the Clouclip device (Glasson Technology Co., Ltd, Hangzhou, China, Wen et al. 2021, 2020). This device has a simple output of only Distance and Illuminance measurements. Data were recorded in 5-second intervals. A weeks worth of data takes up about 1.6 MB of storage.
Spectrum metrics will be calculated using data from a multi-modal device, the Visual Environment Evaluation Tool or VEET (Meta Platforms, Inc., Menlo Park, California, USA, Sah, Narra, and Ostrin 2025). This dense dataset contains distance (spatially resolved), light, activity (accellerometer & gyroscope), and spectrum measurements, recorded in 2-second intervals. A weeks worth of data takes up about 270 MB of storage.

3.4 Data import & preparation

This tutorial will start by importing a Clouclip dataset and providing an overview of the data. The Clouclip export is considerably simpler compared to the VEET device, only containing Distance and Illuminance measurements. The VEET dataset will be imported later for the spectrum related metrics.

# load libraries
library(tidyverse)
library(LightLogR)
library(gt)

LightLogR provides accessible import functionality for many wearable devices (18 at the time of writing). Required information are the file(s) and the time zone the device was set up with/recorded in (default is UTC). Many optional arguments let a user, e.g., extract IDs from the file name or correct for daylight savings jumps. The import also provides a comprehensive overview of the data, letting the user know of any gaps and irregularities in the data.

# import the data
path <- "data/Sample_Clouclip.csv"
tz <- "US/Central"
dataCC <- import$Clouclip(path, tz = tz, manual.id = "Clouclip")


Successfully read in 58'081 observations across 1 Ids from 1 Clouclip-file(s).
Timezone set is US/Central.
The system timezone is Europe/Berlin. Please correct if necessary!

First Observation: 2021-02-06 17:12:47
Last Observation: 2021-02-14 17:12:36
Timespan: 8 days

Observation intervals: 
  Id       interval.time            n pct     
1 Clouclip 5s                   54572 93.9601%
2 Clouclip 17s                     12 0.0207% 
3 Clouclip 18s                     14 0.0241% 
4 Clouclip 120s (~2 minutes)     3479 5.9900% 
5 Clouclip 128s (~2.13 minutes)     1 0.0017% 
6 Clouclip 132s (~2.2 minutes)      1 0.0017% 
7 Clouclip 133s (~2.22 minutes)     1 0.0017%

3.4.1 Exploration

It seems there are many gaps in the data. Understanding how these relate to the measurements and the time of recording is essential. LightLogR provides tools to visualize and summarize these gaps.

In the presence of irregular data, i.e. data that does not fall in a regular sequence of datetimes, gap summaries can be computationally very expensive and inaccurate. For that reason (if it is not already visible from the import summary, it makes sense to check for irregular data)

dataCC |> has_irregulars() #test for irregulars

[1] TRUE

In the case of irregular data, it is recommended to visualize irregulars without recalculating implicit gaps, which are missing observations at regular intervals. See Figure 1.

y.label <- "Distance (cm)"
dataCC |> gg_gaps(Dis, 
                  include.implicit.gaps = FALSE,
                  show.irregulars = TRUE,
                  y.axis.label = y.label,
                  group.by.days = TRUE
                  )

Figure 1: Visualization of gaps and irregular data

It looks like data in every day but the first and last are considered irregular. This happens with some devices and requires manual handling. Strategies include:

Removing some intervals from the start if the irregularities are due to the setup process. See filter_Date() for a way to remove these. This is usually a good solution if only the first day has regular data and the rest is all irregular.
Rounding datetime values to the closest (5 second) interval. See cut_Datetime() for a helper function. This is appropriate if deviations from the dominant interval (5 seconds in this case) are infrequent, all deviations are larger than the dominant interval, and rounded datetimes don’t lead to duplicated datetetimes.
Aggregating data into a coarser recording interval. See aggregate_Datetime() for this option. This is appropriate in most cases, but leads to a loss in granularity.

Based on the import summary, and the graph, we use the second option to deal with the irregular data.

# round observation times to the next 5-second interval
dataCC <-
  dataCC |>
  cut_Datetime("5 secs", New.colname = Datetime) |> 
  group_by(Day = date(Datetime))

# summarize the data
dataCC |> gap_table(Dis, Variable.label = "Distance (cm)")

Table 3: Summary of missing and observed data for the Clouclip device

Summary of available and missing data
Variable: Distance (cm)
	Data							Missing
	Regular			Irregular	Range		Interval	Gaps						Implicit			Explicit
	Time	%	n¹	n^2,1	Time	n¹	Time	N	ø	øn¹	Time	%	n¹	Time	%	n¹	Time	%	n¹
Overall	2d 14h 43m 10s	29.0%³	45,158	0	1w 2d	155,520	5	2,690	1h 35m 57s	1,151	6d 9h 16m 50s	71.0%³	110,362	5d 15h 19m 55s	62.7%³	97,439	17h 56m 55s	8.3%³	12,923
2021-02-06
	43m 30s	3.0%	522	0	1d	17,280	5s	26	53m 43s	645	23h 16m 30s	97.0%	16,758	22h 53m 20s	95.4%	16,480	23m 10s	1.6%	278
2021-02-07
	2h 45m	11.5%	1,980	0	1d	17,280	5s	139	9m 10s	110	21h 15m	88.5%	15,300	19h 42m 35s	82.1%	14,191	1h 32m 25s	6.4%	1,109
2021-02-08
	11h 13m 55s	46.8%	8,087	0	1d	17,280	5s	443	1m 44s	21	12h 46m 5s	53.2%	9,193	10h 47m 50s	45.0%	7,774	1h 58m 15s	8.2%	1,419
2021-02-09
	8h 46m 25s	36.6%	6,317	0	1d	17,280	5s	278	3m 17s	39	15h 13m 35s	63.4%	10,963	13h 50m	57.6%	9,960	1h 23m 35s	5.8%	1,003
2021-02-10
	7h 1m 30s	29.3%	5,058	0	1d	17,280	5s	367	2m 47s	33	16h 58m 30s	70.7%	12,222	14h 18m 40s	59.6%	10,304	2h 39m 50s	11.1%	1,918
2021-02-11
	8h 31m 55s	35.5%	6,143	0	1d	17,280	5s	423	2m 12s	26	15h 28m 5s	64.5%	11,137	11h 43m 30s	48.9%	8,442	3h 44m 35s	15.6%	2,695
2021-02-12
	12h 17m 55s	51.2%	8,855	0	1d	17,280	5s	417	1m 41s	20	11h 42m 5s	48.8%	8,425	9h 17m 45s	38.7%	6,693	2h 24m 20s	10.0%	1,732
2021-02-13
	10h 32m 15s	43.9%	7,587	0	1d	17,280	5s	527	1m 32s	18	13h 27m 45s	56.1%	9,693	10h 42m 10s	44.6%	7,706	2h 45m 35s	11.5%	1,987
2021-02-14
	50m 45s	3.5%	609	0	1d	17,280	5s	70	19m 51s	238	23h 9m 15s	96.5%	16,671	22h 4m 5s	92.0%	15,889	1h 5m 10s	4.5%	782
¹ Number of (missing or actual) observations
² If n > 0: it is possible that the other summary statistics are affected, as they are calculated based on the most prominent interval.
³ Based on times, not necessarily number of observations

Table 3 shows that there are no more irregular data after treatment. There are, however, considerable implicitly missing data, which can be converted to explicitly missing data with gap_handler(), which will make calculations based on the dataset much more robust. Furthermore, there are two days that have less than an hours worth of data. These will be removed.

#make implicit gaps explicit
dataCC <- 
  dataCC |> 
  gap_handler(full.days = TRUE) |>  #make gaps explicit
  remove_partial_data(Dis, threshold.missing = "23 hours")

The Clouclip device uses sentinel values to encode states in the measurement values. LightLogR converts these to a dedicated column, and we can visualize them, alongside showing the photoperiod.

#setting coordinates for Houston, Texas
coordinates <- c(29.75, -95.36)
# visualize observations
dataCC |> 
  gg_day(y.axis = Dis, geom = "line", y.axis.label = y.label) |> #create a basic plot
  gg_state(Dis_status, aes_fill = Dis_status) |> #add the status times
  gg_photoperiod(coordinates) + #add the photoperiod (day/night)
  theme(legend.position = "bottom")

Figure 2: Distance measurements across days. Colored areas show sentinel states of the device and grey areas show nighttime from civil dusk until dawn

With these data, the metrics can be calculated.

4 Results

4.1 Distance

In the following sections, daily values are calculated. The following helper function takes these daily values and calculates averages for weekend, weekday, and mean daily:

to_mean_daily <- function(data, prefix = "average_") {
  data |> 
    ungroup(Day) |> #ungroup by days
    mean_daily(prefix = prefix) |> #calculate the averages
    rename_with(.fn = \(x) str_replace_all(x,"_"," ")) |> #remove underscores
    gt() #table output
}

4.1.1 Total wear time daily

For Total wear time daily, only instances where there is actual distance data available will be taken into account in Table 4.

dataCC |> 
  durations(Dis) |> #calculate the durations per group (day)
  to_mean_daily("Total wear ")

Table 4: Total wear time daily

Day	Total wear duration
Mean daily	31448s (~8.74 hours)
Weekday	34460s (~9.57 hours)
Weekend	23918s (~6.64 hours)

4.1.2 Duration within distance ranges

This metric can be calculated in two ways. Table 5 shows the duration of near work, whereas Table 6 shows the duration of distance ranges.

dataCC |> 
  filter(Dis >= 10, Dis < 60) |> 
  durations(Dis) |> 
  to_mean_daily("Near work ")

Table 5: Duration of near work

Day	Near work duration
Mean daily	22586s (~6.27 hours)
Weekday	26343s (~7.32 hours)
Weekend	13192s (~3.66 hours)

#cutting distance into bands
dist_breaks <- c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100, Inf)
dist_labels <- c(
    "Extremely near",          # [10, 20)
    "Very near",               # [20, 30)
    "Fairly near",             # [30, 40)
    "Near",                    # [40, 50)
    "Moderately near",         # [50, 60)
    "Near Intermediate",       # [60, 70)
    "Intermediate",            # [70, 80)
    "Moderately intermediate", # [80, 90)
    "Far intermediate",        # [90, 100)
    "Far"                      # [100, Inf)
  )

dataCC |> 
  mutate(Dis_range = 
           cut(Dis, breaks = dist_breaks, labels = dist_labels) #create ranges
         ) |> 
  drop_na(Dis_range) |> #remove NAs
  group_by(Dis_range, .add = TRUE) |> #group by ranges
  durations(Dis) |> #calculate durations
  pivot_wider(names_from = Dis_range, values_from = duration) |> #widen data
  to_mean_daily("") |> 
  fmt_duration(input_units = "seconds", output_units = "minutes") #show minutes

Table 6: Duration in distance ranges

Day	Extremely near	Very near	Fairly near	Near	Moderately near	Near Intermediate	Intermediate	Moderately intermediate	Far intermediate	Far
Mean daily	169m	102m	46m	27m	13m	7m	4m	5m	11m	16m
Weekday	180m	128m	60m	36m	16m	7m	6m	6m	14m	20m
Weekend	141m	38m	12m	5m	5m	8m	1m	3m	2m	5m

Figure 3 shows the distribution of relative times within each distance range.

Figure 3: Percentage of total time in distance ranges

4.1.3 Frequency of Continuous near work

Continuous near work has more than one condition. Beyond a distance range, it requires a certain length, but also allows for interruptions. This is what extract_clusters() allows.

Table 7 summarizes the results, and Figure 4 visualizes them.

dataCC |> 
  extract_clusters(Dis >= 20 & Dis < 60, #define the condition
                   cluster.duration = "30 mins", #define the minimum duration
                   interruption.duration = "1 min") |> #define max interruption
  summarize_numeric(remove = c("start", "end", "epoch", "duration"),
                    add.total.duration = FALSE) |> #count the number of episodes
  mean_daily(prefix = "Frequency of ") |># daily means
  gt() |> fmt_number() #table

Table 7: Frequency of continuous near work

Day	Frequency of episodes
Mean daily	0.86
Weekday	1.20
Weekend	0.00

Warning: Removed 65357 rows containing missing values or values outside the scale range
(`geom_line()`).

Figure 4: Continuous near work episodes (red)

4.1.4 Near Work episodes

This section of the metrics consists of three aspects: Frequency, Duration, and Distances. The first two aspects are collected the same way as in the previous section, whereas the Distance aspect is extracted from the base data. All are summarized in Table 8

dataCC |> 
  extract_clusters(Dis >= 20 & Dis < 60, #define the condition
                   cluster.duration = "5 secs", #define the minimum duration
                   interruption.duration = "20 secs") |> #define max interruption
  extract_metric(dataCC, distance = mean(Dis, na.rm = TRUE)) |> 
  summarize_numeric(remove = c("start", "end", "epoch"), prefix = "",
                    add.total.duration = FALSE) |>  #count the number of episodes
  mean_daily(prefix = "") |> #daily means
  gt() |> fmt_number(c(distance, episodes), decimals = 0) #table

Table 8: Frequency, duration, and distance of near work episodes

Day	duration	distance	episodes
Mean daily	233s (~3.88 minutes)	32	57
Weekday	284s (~4.73 minutes)	32	64
Weekend	104s (~1.73 minutes)	32	40

4.1.5 Visual breaks

Visual breaks are a little different, compared to the previous metrics. The difference is that in this case, the minimum break and the previous episode is important. This leads to a two step process, where we first extract instances of Distance above 100 cm for at least 20 seconds, before we filter for a previous duration of at maximum 20 minutes. Table 9 provides the daily frequency of visual breaks.

dataCC |> 
  extract_clusters(Dis <= 100, #define the condition
                   cluster.duration = "20 secs", #define the minimum duration
                   return.only.clusters = FALSE) |> #return non-clusters as well
  filter((start - lag(end) <= duration("20 mins")), is.cluster) |> # return only  
  #clusters with previous episode lengths of maximum 20 minutes
  summarize_numeric(remove = c("start", "end", "epoch", "is.cluster", "duration"), 
                    prefix = "",
                    add.total.duration = FALSE) |>  #count the number of episodes
  mean_daily(prefix = "Daily ") |> #daily means
  gt() |> fmt_number(decimals = 0) #table

Table 9: Frequency of visual breaks

Day	Daily episodes
Mean daily	170
Weekday	183
Weekend	136

4.2 Light

Illuminance values are very low in the example dataset from the Clouclip device, which would not yield satisfying summaries in the Light section. Thus, we will import data from the VEET device next. Because there are different modalities stored in the data, we need to specify which modality we want to access. ALS is the acronym for Ambient Light Sensor.

path <- "data/01_VEET_L.csv"
tz <- "US/Central"
dataVEET <- import$VEET(path, tz = tz, modality = "ALS", manual.id = "VEET")

Warning: There was 1 warning in `dplyr::mutate()`.
ℹ In argument: `dplyr::across(...)`.
Caused by warning:
! NAs introduced by coercion


Successfully read in 304'193 observations across 1 Ids from 1 VEET-file(s).
Timezone set is US/Central.
The system timezone is Europe/Berlin. Please correct if necessary!
1 observations were dropped due to a missing or non-parseable Datetime value (e.g., non-valid timestamps during DST jumps). 

First Observation: 2024-06-04 15:00:37
Last Observation: 2024-06-12 08:29:43
Timespan: 7.7 days

Observation intervals: 
  Id    interval.time              n pct      
1 VEET  0s                         1 0.00033% 
2 VEET  1s                      1957 0.64334% 
3 VEET  2s                    300147 98.67025%
4 VEET  3s                      2074 0.68181% 
5 VEET  4s                         3 0.00099% 
6 VEET  9s                         5 0.00164% 
7 VEET  10s                        3 0.00099% 
8 VEET  109s (~1.82 minutes)       1 0.00033% 
9 VEET  59077s (~16.41 hours)      1 0.00033%

This dataset has gaps and irregular data, similarly to the Clouclip data. For consistency, We will aggregate the data to 5-second intervals and set gaps explicitly. We will also remove days that have more than one hour missing data. Remaining are six days with good data coverage, as seen in Table 10.

dataVEET <-
  dataVEET |>
  aggregate_Datetime(unit = "5 seconds") |> #aggregate to 5 second interval
  gap_handler(full.days = TRUE) |>  #set implicit gaps to explicit gaps
  group_by(Day = date(Datetime)) |> #group data by day
  remove_partial_data(Lux, threshold.missing = "1 hour") #remove bad days

dataVEET |> gap_table(Lux, "Illuminance (lx)")

Table 10: Summary of missing and observed data for the VEET device

Summary of available and missing data
Variable: Illuminance (lx)
	Data							Missing
	Regular			Irregular	Range		Interval	Gaps						Implicit			Explicit
	Time	%	n¹	n^2,1	Time	n¹	Time	N	ø	øn¹	Time	%	n¹	Time	%	n¹	Time	%	n¹
Overall	5d 23h 57m 40s	100.0%³	103,652	0	6d	103,680	5	8	58s	12	2m 20s	0.0%³	28	0s	0.0%³	0	2m 20s	0.0%³	28
2024-06-06
	23h 58m 5s	99.9%	17,257	0	1d	17,280	5s	3	38s	8	1m 55s	0.1%	23	0s	0.0%	0	1m 55s	0.1%	23
2024-06-07
	1d	100.0%	17,280	0	1d	17,280	5s	0	0s	0	0s	0.0%	0	0s	0.0%	0	0s	0.0%	0
2024-06-08
	23h 59m 55s	100.0%	17,279	0	1d	17,280	5s	1	5s	1	5s	0.0%	1	0s	0.0%	0	5s	0.0%	1
2024-06-09
	23h 59m 50s	100.0%	17,278	0	1d	17,280	5s	2	5s	1	10s	0.0%	2	0s	0.0%	0	10s	0.0%	2
2024-06-10
	23h 59m 55s	100.0%	17,279	0	1d	17,280	5s	1	5s	1	5s	0.0%	1	0s	0.0%	0	5s	0.0%	1
2024-06-11
	23h 59m 55s	100.0%	17,279	0	1d	17,280	5s	1	5s	1	5s	0.0%	1	0s	0.0%	0	5s	0.0%	1
¹ Number of (missing or actual) observations
² If n > 0: it is possible that the other summary statistics are affected, as they are calculated based on the most prominent interval.
³ Based on times, not necessarily number of observations

4.2.1 Light exposure

Averages of light exposure can be calculated with just summarize_numeric(). See Table 11

dataVEET |> 
  select(Day, Datetime, Lux) |> 
  summarize_numeric(prefix = "mean ", remove = c("Datetime")) |> 
  mean_daily(prefix = "") |> 
  gt() |> fmt_number(decimals = 1) |> cols_hide(episodes) #table

Table 11: Light exposure (mean) without transformation

Day	mean Lux
Mean daily	304.1
Weekday	357.8
Weekend	169.8

As light exposure data is highly skewed and zero-inflated, however, a transformation is sensible for the mean to be meaningful. The resulting illuminance is commonly much lower, due to the skew and the influence of zero values, as can be seen in Table 12. log_zero_inflated() solves this by adding a small value to the dataset prior to logarithmic transformation. exp_zero_inflated() does the opposite.

dataVEET |> 
  select(Day, Datetime, Lux) |> 
  mutate(Lux = Lux |> log_zero_inflated()) |> #convert to logarithmic data
  summarize_numeric(prefix = "mean ", remove = c("Datetime")) |> 
  mean_daily(prefix = "") |> 
  mutate(`mean Lux` = `mean Lux` |> exp_zero_inflated()) |>
  gt() |> fmt_number(decimals = 1) |> cols_hide(episodes) #table

Table 12: Light exposure (mean) with logarithmic transformation

Day	mean Lux
Mean daily	6.3
Weekday	7.9
Weekend	3.5

4.2.2 Duration per outdoor range

The same way how distance ranges were calculated, illuminance ranges are summarized, displayed in Table 13.

#cutting distance into bands
out_breaks <- c(1:3*10^3, Inf)
out_labels <- c(
    "Outdoor bright",          # [1000, 2000)
    "Outdoor very bright",     # [2000, 3000)
    "Outdoor extremely bright" # [3000, Inf)
  )

dataVEET <- 
dataVEET |> 
  mutate(Lux_range = 
           cut(Lux, breaks = out_breaks, labels = out_labels) #create ranges
         )

dataVEET |> 
  drop_na(Lux_range) |> #remove NAs
  group_by(Lux_range, .add = TRUE) |> #group by ranges
  durations(Lux) |> #calculate durations
  pivot_wider(names_from = Lux_range, values_from = duration) |> #widen data
  to_mean_daily("") |> 
  fmt_duration(input_units = "seconds", output_units = "minutes") #show minutes

Table 13: Duration in outdoor ranges

Day	Outdoor bright	Outdoor very bright	Outdoor extremely bright
Mean daily	24m	32m	55m
Weekday	29m	41m	65m
Weekend	10m	10m	30m

These states can also be easily visualized in Figure 5.

dataVEET |> 
  gg_day(y.axis = Lux, 
         y.axis.label = "Illuminance (lx)", 
         geom = "line", 
         jco_color = FALSE) |> 
  gg_state(Lux_range, aes_fill = Lux_range, alpha = 0.75) |> 
  gg_photoperiod(coordinates) +
  scale_fill_viridis_d() +
  labs(fill = "Illuminance conditions") +
  theme(legend.position = "bottom")

Figure 5: Outdoor ranges throughout the measurement period

4.2.3 Changes indoor to outdoor

To calculate the number of times a change from indoor to outdoor happens, we can extract all states where this is the case in Table 14.

dataVEET |> 
  extract_states(Outdoor, Lux >= 1000, #get all instances of states and non-states
                 group.by.state = FALSE) |> #don't group output by the state 
  filter(!lead(Outdoor), Outdoor) |>  #keep where the prior state is FALSE
  summarize_numeric(
    prefix = "mean ",
    remove = c("Datetime", "Outdoor", "start", "end", "duration"),
    add.total.duration = FALSE
    ) |> 
  mean_daily(prefix = "") |> 
  gt() |> fmt_number(episodes, decimals = 0) #table

Table 14: Number of times light levels change from indoor (<1000 lx) to outdoor (>1000 lx)

Day	mean epoch	episodes
Mean daily	5s	64
Weekday	5s	72
Weekend	5s	46

This seems rather high and is certainly influenced by the small interval of 5 seconds. Requiring that the time outside has to at least persist for 5 minutes (and slight interruptions) will bring this number down. See Table 15 for comparison.

dataVEET |> 
  extract_clusters(Lux >= 1000, #cluster conditions
                   cluster.duration = "5 min", #require 1 minute durations
                   interruption.duration = "20 secs", #allow for short interruptions
                   return.only.clusters = FALSE) |> #get all instances of clusters and non-clusters
  filter(!lead(is.cluster), is.cluster) |>  #keep where the prior state is FALSE
  summarize_numeric(
    prefix = "mean ",
    remove = c("Datetime", "start", "end", "duration"),
    add.total.duration = FALSE
    ) |> 
  mean_daily(prefix = "") |> 
  gt() |> fmt_number(episodes, decimals = 0) #table

Table 15: Number of times light levels change from indoor (<1000 lx) to outdoor (>1000 lx) for at least 5 minutes

Day	mean epoch	episodes
Mean daily	5s	5
Weekday	5s	6
Weekend	5s	4

4.2.4 Longest period above 1000 lx

The last Light aspect from Table 1 is the longest period above 1000 lx (PAT1000). While this can be calculated based on what we have shown above by combining extract_states() with a simple filter for maximal duration, this metric provides a good opportunity to show that some aspects can also be calculated with dedicated metric functions in LightLogR. In this case, we use period_above_threshold(). The benefit of this approach is that multiple metrics can be calculated at once. Here, for example, we also calculate the duration above 1000 lx (TAT1000) alongside in Table 16

dataVEET |> 
  summarize(PAT1000 = 
              period_above_threshold(Lux, 
                                     Datetime, 
                                     threshold = 1000, 
                                     na.rm = TRUE),
            TAT1000 =
              duration_above_threshold(Lux,
                                       Datetime,
                                       threshold = 1000,
                                       na.rm = TRUE),
            .groups = "drop_last") |> 
  mean_daily(prefix = "") |> 
  gt()

Table 16: Longest period (PAT1000) and total duration (TAT1000) above 1000 lx

Day	PAT1000	TAT1000
Mean daily	1987s (~33.12 minutes)	6709s (~1.86 hours)
Weekday	2501s (~41.68 minutes)	8164s (~2.27 hours)
Weekend	702s (~11.7 minutes)	3070s (~51.17 minutes)

4.3 Spectrum

Spectral data is not part of any of the datasets used in this article. Rather, it has to be reconstructed from sensor counts and a calibration matrix. The VEET device contains ten sensor channels that can be used for reconstruction. As these are contained in a different sensor than the ambient light sensor, a different modality needs to be imported from the same file. Data preparation will be analogous to Light. PHO contains the data from spectral sensor channels. For computational reasons, the data will be aggregated to 5 minutes intervals. The first three rows are shown in Table 17.

dataVEET <- import$VEET(path, tz = tz, modality = "PHO", manual.id = "VEET")


Successfully read in 304'197 observations across 1 Ids from 1 VEET-file(s).
Timezone set is US/Central.
The system timezone is Europe/Berlin. Please correct if necessary!

First Observation: 2024-06-04 15:00:36
Last Observation: 2024-06-12 08:29:43
Timespan: 7.7 days

Observation intervals: 
   Id    interval.time              n pct      
 1 VEET  0s                         1 0.00033% 
 2 VEET  1s                      1753 0.57627% 
 3 VEET  2s                    300556 98.80340%
 4 VEET  3s                      1873 0.61572% 
 5 VEET  4s                         3 0.00099% 
 6 VEET  6s                         1 0.00033% 
 7 VEET  7s                         2 0.00066% 
 8 VEET  9s                         5 0.00164% 
 9 VEET  109s (~1.82 minutes)       1 0.00033% 
10 VEET  59077s (~16.41 hours)      1 0.00033%

dataVEET <-
  dataVEET |>
  aggregate_Datetime(unit = "5 mins") |> #aggregate to 5 minute intervals
  gap_handler(full.days = TRUE) |>  #set implicit gaps to explicit gaps
  group_by(Day = date(Datetime)) |> #group data by day
  remove_partial_data(Gain, threshold.missing = "1 hour") #remove bad days

dataVEET |> head(3) |> gt() |> fmt_number(s415:ClearR)

Table 17: Overview of the spectral sensor import from the VEET device

Id	Datetime	is.implicit	time_stamp	integration_time	Gain	s415	s445	s480	s515	s555	s590	s630	s680	s940	Dark	ClearL	ClearR	file.name	modality
2024-06-06
VEET	2024-06-06	FALSE	1717649999	50	512	0.14	4.77	5.89	12.95	27.06	41.85	54.49	33.61	14.67	0.00	92.84	95.39	01_VEET_L	PHO
VEET	2024-06-06 00:05:00	FALSE	1717650299	50	512	0.14	4.69	5.87	12.95	26.82	41.33	54.07	33.25	14.69	0.00	91.93	94.81	01_VEET_L	PHO
VEET	2024-06-06 00:10:00	FALSE	1717650599	50	512	0.14	4.73	5.94	12.98	27.39	42.43	55.07	33.80	15.15	0.00	93.63	96.57	01_VEET_L	PHO

The channels s415 through ClearR contain raw sensor counts and need to be normalized by the Gain value. Further, ClearL and ClearR need to be averaged prior to spectral reconstruction. The appropriate gain.ratio.table for the sensor TSL2585 is integrated in LightLogR, but should also be confirmed by the manufacturer.

count.columns <- c("s415", "s445", "s480", "s515", "s555", "s590", "s630", 
                      "s680", "s940", "Dark", "ClearL", "ClearR") #column names
#normalize data
dataVEET <-
  dataVEET |> 
  normalize_counts( #function to normalize counts
    gain.columns = rep("Gain", 12), #all sensor channels share the gain value
    count.columns = count.columns, #senso channels to normalize
    gain.ratio.tables$TSL2585 #gain ratio channel for TSL2585 sensor
  )
#average Clear Channels
dataVEET <- 
  dataVEET |> 
  mutate(Clear.normalized = (ClearL+ClearR)/2)

#remove raw sensor counts and rename normalized values
dataVEET <-
  dataVEET |> 
    select(-c(s415:ClearR)) |> 
    rename_with(\(x) str_remove(x, ".normalized"))

This closes the necessary preparation in the dataset. The calibration matrix was provided by the manufacturer and is specific to the make and model. It should not be used for research purposes without confirming its accuracy with the manufacturer.

#import calibration matrix
calib_mtx <- 
  read_csv("data/VEET_calibration_matrix.csv", show_col_types = FALSE) |> 
  column_to_rownames("wavelength") |> 
  as.matrix()

Construction the spectrum is now straightforward.

dataVEET <-
  dataVEET |> 
    mutate(Spectrum = 
             spectral_reconstruction(
               sensor_channels = pick(s415:s940, Clear),
               calibration_matrix = calib_mtx
               )
           )

The dataset now contains a list-column containing the spectrum for each observation. We can visualize the data in Figure 6.

dataVEET |>
  unnest(Spectrum) |> #unnest the list column
  group_by(Datetime) |> #group by each spectrum
  mutate(irradiance = irradiance/max(irradiance)) |> #scale spectra relative
  ggplot(aes(x=wavelength, y = irradiance, group = Datetime)) + #plot
  geom_path(alpha = 0.15) +
  theme_minimal() +
  labs(y = "Relative spectral irradiance (%)", x = "Wavelength (nm)") + 
  scale_y_continuous(labels = scales::label_percent())+
  coord_cartesian(xlim = c(400, 700), ylim = c(0,1), expand = FALSE) +
  theme(plot.margin = margin(10,20,10,10))

These spectral data will be the basis to calculate the last two metrics.

4.3.1 Ratio of short vs. long wavelength light

The first spectral metric requires integration across two sections of the spectrum. spectral_integration() makes this task straight forward. The results can be seen in Table 18

dataVEET <- 
dataVEET |> 
  select(Day, Datetime, Spectrum) |> 
  mutate(
    short = Spectrum |> map_dbl(spectral_integration, #short wavelength
                                   wavelength.range = c(400,500)),
    long = Spectrum |> map_dbl(spectral_integration, #long wavelength
                                  wavelength.range = c(600,700)),
    `sl ratio` = short / long # calculate the ratio
  )

dataVEET |> 
  summarize_numeric(prefix = "", remove = c("Datetime", "Spectrum")) |> 
  mean_daily(prefix = "") |>
  gt() |> fmt_number(-`sl ratio`, decimals = 0) |> cols_hide(episodes) # table

Table 18: Ratio of short vs. long wavelength light

Day	short	long	sl ratio
Mean daily	37	102	-0.7410509
Weekday	70	135	-0.5457927
Weekend	−46	20	-1.2291965

4.3.2 Short-wavelength light at certain times of day

For the last metric we will look at only the short wavelength contribution (which was already calculated in the previous section), but do so through certain times of day. Table 19 shows the first approach, which is about exclusively looking at local time. Figure 7 expands this view to all hours of the day with a binned approach. Lastly, Table 20 focuses photoperiods.

dataVEET |> 
  filter_Time(start = "11:00:00", end = "14:00:00") |> #filter out certain times
  select(-c(Spectrum, long, `sl ratio`, Time.data, Datetime)) |> 
  summarize_numeric(prefix = "") |> 
  mean_daily(prefix = "") |> 
  gt() |> fmt_number(short) |> cols_label(short = "Short wavelength irradiance")

Table 19: Short wavelength light exposure between 11:00 and 14:00

Day	Short wavelength irradiance	episodes
Mean daily	−184.21	37
Weekday	−202.23	37
Weekend	−139.14	37

#creating the data
dataVEETtime <-
  dataVEET |>
  cut_Datetime(unit = "1 hour", #create time sections of one hour
               type = "floor",
               group_by = TRUE) |>
  select(-c(Spectrum, long, `sl ratio`, Datetime)) |>
  summarize_numeric(prefix = "") |>
  group_by(Datetime.rounded, .drop = FALSE) |> #group by the time state
  mean_daily(prefix = "", sub.zero = TRUE) |>
  create_Timedata(Datetime.rounded) #add a time column for plotting

#creating the plot
dataVEETtime |> 
  ggplot(aes(x=Time.data, y = short/max(short))) +
  geom_col(aes(fill = Day), position = "dodge") +
  ggsci::scale_fill_jco() +
  theme_minimal() +
  labs(y = "Relative short wavelength contribution (%)", 
       x = "Local time (HH:MM)") + 
  scale_y_continuous(labels = scales::label_percent()) +
  scale_x_time(labels = scales::label_time(format = "%H:%M"))

Figure 7: Short wavelength light exposure across the day

dataVEET |>
  select(-c(Spectrum, long, `sl ratio`)) |>
  add_photoperiod(coordinates) |> 
  group_by(photoperiod.state, .add = TRUE) |> 
  summarize_numeric(prefix = "", 
                    remove = c("dawn", "dusk", "photoperiod", "Datetime")) |> 
  group_by(photoperiod.state) |> 
  mean_daily(prefix = "") |> 
  select(-episodes) |> 
  pivot_wider(names_from =photoperiod.state, values_from = short) |> 
  gt() |> fmt_number()

Table 20: Short wavelength light exposure during the day and at night

Day	day	night
Mean daily	80.14	−27.73
Weekday	140.09	−33.96
Weekend	−69.73	−12.17

5 Discussion and conclusion

This tutorial demonstrated how to derive at various metrics used in current and future research in a principled and standardized approach. While not brief overall, each metric has a dedicated pipeline to derive at the summaries that is easy to understand. Those pipelines utilize LightLogRs framework and combine that with common data analysis workflows. The goal is to make the process transparent (function definitions open source), accessible (sound documentation, tutorials, speaking function and argument names, MIT license), robust (over 400 unit tests for functions, continuous integration on GitHub, bug-tracking on github), and community-driven (feature tracking on GitHub, open process for researchers who want to contribute code or suggest features).

The tutorial also demonstrated that even through these standardized pipelines, there are many decisions a researcher has to make (and document) to clean data, deal with measurement epochs, and derive at the metrics, especially where clusters of data are concerned.

The slew of features aimed to explore the data and extracted metrics or clusters in plots and tables, and to handle measurement intervals, gaps, and irregular data, make LightLogR an excellent choice for the research field of visual experience, be it in circadian, myopia, or related fields of research.

6 Session info

sessionInfo()

R version 4.4.3 (2025-02-28)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Berlin
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gt_1.0.0        LightLogR_0.9.0 lubridate_1.9.4 forcats_1.0.0  
 [5] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.4     readr_2.1.5    
 [9] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.2   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.52          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] vctrs_0.6.5        tools_4.4.3        generics_0.1.3     parallel_4.4.3    
 [9] proxy_0.4-27       pkgconfig_2.0.3    KernSmooth_2.23-26 RColorBrewer_1.1-3
[13] lifecycle_1.0.4    compiler_4.4.3     farver_2.1.2       suntools_1.0.1    
[17] ggsci_3.2.0        janitor_2.2.1      snakecase_0.11.1   litedown_0.7      
[21] class_7.3-23       htmltools_0.5.8.1  sass_0.4.10        yaml_2.3.10       
[25] pillar_1.10.2      crayon_1.5.3       classInt_0.4-11    commonmark_1.9.5  
[29] tidyselect_1.2.1   digest_0.6.37      stringi_1.8.7      sf_1.0-20         
[33] labeling_0.4.3     cowplot_1.1.3      fastmap_1.2.0      grid_4.4.3        
[37] archive_1.1.12     cli_3.6.5          magrittr_2.0.3     base64enc_0.1-3   
[41] utf8_1.2.4         e1071_1.7-16       withr_3.0.2        scales_1.4.0      
[45] bit64_4.6.0-1      timechange_0.3.0   rmarkdown_2.29     bit_4.6.0         
[49] ggtext_0.1.2       hms_1.1.3          evaluate_1.0.3     knitr_1.50        
[53] viridisLite_0.4.2  markdown_2.0       rlang_1.1.6        gridtext_0.1.5    
[57] Rcpp_1.0.14        glue_1.8.0         DBI_1.2.3          xml2_1.3.8        
[61] rstudioapi_0.17.1  vroom_1.6.5        jsonlite_2.0.0     R6_2.6.1          
[65] units_0.8-7

7 References

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Biller, A. M., P. Balakrishnan, and M. Spitschan. 2024. “Behavioural Determinants of Physiologically-Relevant Light Exposure.” Journal Article. Commun Psychol 2 (1): 114. https://doi.org/10.1038/s44271-024-00159-5.

Blume, C., C. Garbazza, and M. Spitschan. 2019. “Effects of Light on Human Circadian Rhythms, Sleep and Mood.” Journal Article. Somnologie (Berl) 23 (3): 147–56. https://doi.org/10.1007/s11818-019-00215-x.

Brown, T. M., G. C. Brainard, C. Cajochen, C. A. Czeisler, J. P. Hanifin, S. W. Lockley, R. J. Lucas, et al. 2022. “Recommendations for Daytime, Evening, and Nighttime Indoor Light Exposure to Best Support Physiology, Sleep, and Wakefulness in Healthy Adults.” Journal Article. PLoS Biol 20 (3): e3001571. https://doi.org/10.1371/journal.pbio.3001571.

Dahlmann-Noor, A. H., D. Bokre, M. Khazova, and L. L. A. Price. 2025. “Measuring the Visual Environment of Children and Young People at Risk of Myopia: A Scoping Review.” Journal Article. Graefes Arch Clin Exp Ophthalmol. https://doi.org/10.1007/s00417-024-06719-z.

Gibaldi, A., E. N. Harb, C. F. Wildsoet, and M. S. Banks. 2024. “A Child-Friendly Wearable Device for Quantifying Environmental Risk Factors for Myopia.” Journal Article. Transl Vis Sci Technol 13 (10): 28. https://doi.org/10.1167/tvst.13.10.28.

Hartmeyer, S. L., and M. Andersen. 2023. “Towards a Framework for Light-Dosimetry Studies: Quantification Metrics.” Journal Article. Lighting Research & Technology 56 (4): 337–65. https://doi.org/10.1177/14771535231170500.

Hartmeyer, S. L., F. S. Webler, and M. Andersen. 2022. “Towards a Framework for Light-Dosimetry Studies: Methodological Considerations.” Journal Article. Lighting Research & Technology 55 (4-5): 377–99. https://doi.org/10.1177/14771535221103258.

Hönekopp, A., and S. Weigelt. 2023. “Using Light Meters to Investigate the Light-Myopia Association - a Literature Review of Devices and Research Methods.” Journal Article. Clin Ophthalmol 17: 2737–60. https://doi.org/10.2147/OPTH.S420631.

Mohamed, A., V. Kalavally, S. W. Cain, A. J. K. Phillips, E. M. McGlashan, and C. P. Tan. 2021. “Wearable Light Spectral Sensor Optimized for Measuring Daily Alpha-Opic Light Exposure.” Journal Article. Opt Express 29 (17): 27612–27. https://doi.org/10.1364/OE.431373.

Okudaira, N., D. F. Kripke, and J. B. Webster. 1983. “Naturalistic Studies of Human Light Exposure.” Journal Article. Am J Physiol 245 (4): R613–5. https://doi.org/10.1152/ajpregu.1983.245.4.R613.

Sah, Raman Prasad, Pavan Kalyan Narra, and Lisa A. Ostrin. 2025. “A Novel Wearable Sensor for Objective Measurement of Distance and Illumination.” Ophthalmic and Physiological Optics 00 (n/a): 1–13. https://doi.org/https://doi.org/10.1111/opo.13523.

Webler, F. S., M. Spitschan, R. G. Foster, M. Andersen, and S. N. Peirson. 2019. “What Is the ’Spectral Diet’ of Humans?” Journal Article. Curr Opin Behav Sci 30: 80–86. https://doi.org/10.1016/j.cobeha.2019.06.006.

Wen, Longbo, Yingpin Cao, Qian Cheng, Xiaoning Li, Lun Pan, Lei Li, HaoGang Zhu, Weizhong Lan, and Zhikuan Yang. 2020. “Objectively Measured Near Work, Outdoor Exposure and Myopia in Children.” British Journal of Ophthalmology 104 (11): 1542–47. https://doi.org/10.1136/bjophthalmol-2019-315258.

Wen, Longbo, Qian Cheng, Yingpin Cao, Xiaoning Li, Lun Pan, Lei Li, Haogang Zhu, Ian Mogran, Weizhong Lan, and Zhikuan Yang. 2021. “The Clouclip, a Wearable Device for Measuring Near-Work and Outdoor Time: Validation and Comparison of Objective Measures with Questionnaire Estimates.” Acta Ophthalmologica 99 (7): e1222–35. https://doi.org/https://doi.org/10.1111/aos.14785.

Wen, Longbo, Qian Cheng, Weizhong Lan, Yingpin Cao, Xiaoning Li, Yiqiu Lu, Zhenghua Lin, Lun Pan, Haogang Zhu, and Zhikuan Yang. 2019. “An Objective Comparison of Light Intensity and Near-Visual Tasks Between Rural and Urban School Children in China by a Wearable Device Clouclip.” Translational Vision Science & Technology 8 (6): 15–15. https://doi.org/10.1167/tvst.8.6.15.

Williams, Rachel, Suyash Bakshi, Edwin J Ostrin, and Lisa A Ostrin. 2019. “Continuous Objective Assessment of Near Work.” Scientific Reports 9 (1): 6901.

Zauner, J., S. Hartmeyer, and M. Spitschan. 2025. “LightLogR: Reproducible Analysis of Personal Light Exposure Data.” Journal Article. J Open Source Softw 10 (107): 7601. https://doi.org/10.21105/joss.07601.

Zauner, Ljiljana AND Spitschan, Johannes AND Udovicic. 2024. “Power Analysis for Personal Light Exposure Measurements and Interventions.” PLOS ONE 19 (12): 1–15. https://doi.org/10.1371/journal.pone.0308768.

Footnotes

Functions from LightLogR are presented as links to the function documentation. General analysis functions (from package dplyr) are presented as normal text.↩︎
This deviates from the common definition of luminous exposure, which is the sum of illuminance measurements scaled to hourly observation intervals↩︎